Glossary

Analysis of variance. Used to perform variable screening by identifying insignificant variables.Variable regression coefficients are ranked based on their significance as obtained through a partial F-test.(See also variable screening).

The total error – the difference between the exact and computed response - is composed of a random and a bias component. The bias component is a systematic deviation between the chosen model (approximation type) and the exact response of the structure (FEA analysis is usually considered to be the exact response). Also known as the modeling error. (See also random error).

A set of Neural Networks of the same order constructed using the same set of results. The nets are usually slightly different because a different weight initiator is typically used for the regression procedure of each individual net.

The interval in which a parameter may occur with a specified level of confidence. Computed using Student’s t-test. Typically applied to accompany the significance of a variable in the form of an error bar.

A curve obtained by using the two ordinate values at a coinciding abscissa obtained from two separate functions. The two ordinate values are used as the abscissa and ordinate in the new crossplot. In LS-OPT two separate time histories are typically used to construct a single crossplot.

A region in the n-dimensional space of the design variables (x1 through xn to which the design is limited. The design space is specified by upper and lower bounds on the design variables. Response variables can also be used to bound the design space.

An area of analysis requiring a specific set of simulation tools, usually because of the unique nature of the physics involved, e.g. structural dynamics or fluid dynamics. In the context of MDO, often used interchangeably with solver.